The field of the invention relates generally to learning systems and methods, and more particularly to systems which may be implemented using multimedia computer technology. The system and method of the present invention may be used for instruction in any number of subjects. Some aspects may be particularly useful in fields where teaching complex visuospatial concepts is required. Others are applicable whenever there is some set of items to be committed to memory.
Instructional and teaching systems have been in existence for centuries, but their development has increased significantly with the development of the digital computer and more recently with the development of multimedia technology. Presently, computers have been implemented in the learning process in many ways. Systems which present a series of static lessons separated by a prompt-response testing procedure which determines whether a student will be allowed to progress to the next lesson or return to additional instruction on the tested subject in another format are known. These methods monitor student progress and disseminate additional information as the student progresses. Also known are learning systems with material indexed by type and degree of difficulty, where the system selects an appropriate lesson according to user input and edits out parts of the lesson which are considered below the student's comprehension level. Other learning systems employ computer technology, but are limited in scope to particular fields of instruction, such as instruction in the use of computer programs, or are limited in format to specific media, such as text and simulation exercises.
Some prior art learning systems utilize a static lesson format which is typically arranged in a predefined order. This format forces each student to conform to a particular lesson format, presented in a particular order, which may not fit his or her specific needs. Recently, attempts have been made to overcome the drawbacks of the prior art by using computer technology to implement learning systems that dynamically adjust to the ability of the student in order to improve and/or accelerate the learning process.
Some recent attempts to develop dynamically adaptable learning systems have used a student's speed and accuracy in answering questions as criteria for changing the problems presented to a particular student. One such learning system is discussed in U.S. Pat. No. 6,077,085, entitled “Technology Assisted Learning,” issued to Parry et al. This reference discloses a learning system directed towards language instruction. The subject matter to be taught is subdivided into sets of conceptually related questions. Exemplary subjects are grammar principles, phrases, and vocabulary. Each set of conceptually related questions is spread across introductory, working, and test “pools” of questions. The program includes a question advancement/regression feature where a period of days must pass before questions from the introductory and working pools are presented to the student in the test pool. This feature is alleged to allow the program to assess whether the student has retained the subject matter in long term memory. In the test pool, questions are presented to the student sequentially and the student's mastery of the subject matter is evaluated based upon whether the student correctly answers each question and upon the relative speed of each correct response. If the student correctly answers the questions within predetermined time constraints, the questions are advanced into a review pool for future review. If a student struggles with a particular question, the question is regressed to a pool where the subject matter represented by the question may be taught in an easier manner. As questions are answered, the system calculates a dynamic average response time for the collective group of correct answers. In determining whether particular subject matter has been successfully mastered, the method compares the response time for questions about the particular subject matter to the student's dynamic average response time. The extent of advancement or regression through multiple question pools is a function of the particular question response time and the dynamic average response time.
Although Parry may be an improvement over prior art methods, the system has several potential drawbacks which provide less than optimal learning instruction. One potential drawback of Parry is that speed and accuracy in answering questions are only used to advance or regress questions from the current working pool. Within the working pool, Parry does not provide a mechanism for presenting questions to students in an order or arrangement most likely to lead to optimal learning based on the student's past answers to questions. Rather Parry repeats questions in a random sequence which is unlikely to lead to enhanced learning and provides little improvement over the prior art. Another drawback of Parry may be that the system will remove questions from the working pool based on a single correct answer on the first trial. The correctly answered question is moved to a review pool for review on a subsequent day in the belief that a delay of one or more days between repeating correctly answered questions improves long term memory. One problem with this approach is that the correct answer may have been the result of a guess. A single trial may often be insufficient to discriminate between learned and guessed answers. In addition, recent research indicates that long term memory is improved by slowly stretching the retention interval for learned questions. Thus, a new and preferable approach would be to repeat questions or problem types at increasing delay intervals and to remove the question from the working group only after the question has been correctly answered in multiple trials, where each trial occurs after a longer delay than the preceding trial.
In this context, a learning format that dynamically adapts to the strengths and weaknesses of each student may be desirable. Preferably, such a system may sequence the appearance order of learning items presented to a student in such a manner as to promote rapid learning of the subject matter. In addition, the learning system may be optimized for the development of long term memory. Ideally, the learning system may include the ability to retire well learned questions from the sequence after certain delay, repetition and success criteria are met. Also, such a system may include the ability to provide for the judicious use of hints to guide students to correct answers.
Another feature of existing learning systems is that they target specific, concrete items of learning, such as learning the Spanish equivalent of the English word “bread,” or deciding whether a certain speech sound is an ‘r’ or an ‘l’. Many important learning tasks involve grasping of some more abstract structure that applies to many different instances. An example would be the learning of particular transformations in algebra that allow one to derive new expressions from old. Such transformations, such as the distributive property of multiplication (a(b+c)=ab+ac, where a, b and c can be any constants, variables or more complicated expressions), are not learned when one has memorized a specific example. Rather, one learns to see the distributive structure in many different contexts. Other examples would be learning to sort leaves of two different species of plants, or the classification of chemical structures into chemical families, or the determination of pathology vs. normal variation in mammograms, in which many properties vary across individual cases.
These aspects of learning are generally not addressed in the existing art of computer-based learning technology. Most often, learning targets specific items of declarative knowledge. Learning structures, abstract patterns, or the determinants of important classifications is not optimized, and may be impeded, by typical formats in the prior art. The reason is that any specific instance of a structure, or any small set of instances, will have individual characteristics that are not part of the concept to be learned. New techniques of learning are required to help the learner extract the invariant or diagnostic structural features or relations that define the concept. A learner who knows what a tractor looks like can correctly classify new tractors despite variations in their color, size and specific features (e.g., he or she can even recognize a miniature, toy tractor without prior experience). A learner who is just learning the term “tractor” in connection with only one or a couple of examples may think that the concept requires that the item be yellow, or have a certain size, etc. As predicted by concepts of simple associative learning, incidental accompanying features will be connected to the item learned. Thus, when a radiologist trainee sees a certain example of pathology in a mammogram, and the pathological part lies in the upper left quadrant of the left breast, and is a 1 cm nodule, he or she will have an implicit tendency to associate all of those features with the diagnosis of pathology. Yet, the actual structural features that determine pathology have little to do with the exact location or size, but rather with properties of shape and texture in the image.
A system for the learning of invariant or diagnostic structure, as opposed to memorization of instances, may desirably be built using different techniques from those in the prior art. Specifically, such a learning system would contain a set of learning instances for each concept to be learned, such that examples of the same concept varied in their irrelevant features. The learning system would preferably require the learner to make many classifications of varying instances, and feedback would be provided. This kind of learning format allows a filtering process to occur, leading to discovery of the diagnostic structures or patterns, while extracting them becomes more efficient and automatic. This kind of learning system exploits the ability of the human attentional system to extract invariant or diagnostic structure from among irrelevant variation. Much of what is learned this way is implicit and not verbalizable; thus, it cannot be taught well through lectures or computer-based tutorial formats that emphasize declarative knowledge (explicit facts and concepts). Yet, this fluent pickup of structure and efficient classification—called perceptual learning or structure learning—are important parts of expertise in almost every learning domain. However, systematic techniques to utilize this ability in learning technology have not been previously developed. Such systems would preferably aid learning in many contexts, including science, mathematics, language and many professional and commercial applications. Because they encourage extraction of diagnostic structure, they would be well suited for teaching not only structure in a domain, but structure mappings across multiple representations, such as graphs and equations in mathematics, or molecular structures and notation in chemistry.